PCB Defect Detection Using Yolov5 on Raspberry-Pi

Abstract: Conventional PCB fault detection are inefficient and error-prone, which tells us that automation is necessary. For this research, YOLOv5 is used onRaspberry Pi to detect sixcategories of PCB defects including MouseBites, Open Circuits,andShort Circuits in real-time. This system is offline capable and provides fast offline detection, accuracy, .and low power optimization. Its accuracyisconfirmedthroughprecision,recall,andmAPperformancemeasures.FindingsindicatethatAIdefectdetectionimproves defect detection significantly, and thus greatly increases PCB quality control while minimizing costs and reducing the ineffecient manual inspections